The interferon (IFN)‐induced double‐stranded RNA (dsRNA)‐activated Ser/Thr protein kinase (PKR) plays a role in the antiviral and antiproliferative effects of IFN. PKR phosphorylates initiation factor eIF2α, thereby inhibiting protein synthesis, and also activates the transcription factor, nuclear factor‐κB (NF‐κB), by phosphorylating the inhibitor of NF‐κB, IκB. Mice devoid of functional PKR (Pkr°/°) derived by targeted gene disruption exhibit a diminished response to IFN‐γ and poly(rI:rC) (pIC). In embryo fibroblasts derived from Pkr°/° mice, interferon regulatory factor 1 (IRF‐1) or guanylate binding protein (Gbp) promoter–reporter constructs were unresponsive to IFN‐γ or pIC but response could be restored by co‐transfection with PKR. The lack of responsiveness could be attributed to a diminished activation of IRF‐1 and/or NF‐κB in response to IFN‐γ or pIC. Thus, PKR acts as a signal transducer for IFN‐stimulated genes dependent on the transcription factors IRF‐1 and NF‐κB.

Apoptosis occurs in response to different cellular stresses, including viral infection, inf lammatory cytokines, growth factor deprivation, and UV light, but it is unclear whether these inducers share a common mechanism of induction. The interferon-induced, double-stranded RNAactivated protein kinase (PKR) has been implicated in processes that rely on apoptosis as control mechanisms in vivo, including antiviral activities, cell growth regulation, and tumorigenesis. Here we report that mouse embryo fibroblasts from mutant mice containing homozygous deletions in the PKR gene (Pkr o/o mice) were resistant to apoptotic cell death in response to double-stranded RNA, tumor necrosis factor-␣, or lipopolysaccharide. The mechanism underlying the suppression of apoptosis in the Pkr o/o cells could be attributed to defects in the activation of DNA-binding activity for the transcription factor interferon regulatory factor-1 and in Fas mRNA induction. Thus, these results provide genetic evidence implicating a requirement for PKR in mediating different forms of stress-related apoptosis.

Ensemble learning is an intensively studies technique in machine learning and pattern recognition. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures. The aim of this article is twofold. First, it is to provide a review of the most widely used ensemble learning methods and their application in various bioinformatics problems, including the main topics of gene expression, mass spectrometry-based proteomics, gene-gene interaction identification from genome-wide association studies, and prediction of regulatory elements from DNA and protein sequences. Second, we try to identify and summarize future trends of ensemble methods in bioinformatics. Promising directions such as ensemble of support vector machine, meta-ensemble, and ensemble based feature selection are discussed.

IAPs (inhibitors of apoptosis) are a family of proteins containing one or more characteristic BIR domains. These proteins have multiple biological activities that include binding and inhibiting caspases, regulating cell cycle progression, and modulating receptor-mediated signal transduction. Our recent studies found the IAP family members XIAP and c-IAP1 are ubiquitinated and degraded in proteasomes in response to apoptotic stimuli in T cells, and their degradation appears to be important for T cells to commit to death. In addition to three BIR domains, each of these IAPs also contains a RING finger domain. We found this region confers ubiquitin protease ligase (E3) activity to IAPs, and is responsible for the auto-ubiquitination and degradation of IAPs after an apoptotic stimulus. Given the fact that IAPs can bind a variety of proteins, such as caspases and TRAFs, it will be of interest to characterize potential substrates of the E3 activity of IAPs and the effects of ubiquitination by IAPs on signal transduction, cell cycle, and apoptosis.